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Title:

Learning Stable Gaussian Process State Space Models

Document type:
Konferenzbeitrag
Author(s):
J. Umlauft; A. Lederer; S. Hirche
Abstract:
Data-driven nonparametric models gain importance as control systems are increasingly applied in domains where classical system identification is difficult, e.g., because of the system's complexity, sparse training data or its probabilistic nature. Gaussian process state space models (GP-SSM) are a data-driven approach which requires only high-level prior knowledge like smoothness characteristics. Prior known properties like stability are also often available but rarely exploited duri...     »
Keywords:
conhumo; data_driven_control; armin_lederer
FP7 Projekt ID:
337654
Book / Congress title:
American Control Conference (ACC)
Organization:
IEEE
Publisher:
IEEE
Year:
2017
Year / month:
2017-05
Month:
May
Pages:
6
Reviewed:
ja
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